Recent adversarial attacks with real world applications are capable of deceiving deep neural networks (DNN), which often appear as printed stickers applied to objects in physical world. Though achieving high success rate in lab tests and limited field tests, such attacks have not been tested on multiple DNN architectures with a standard setup to unveil the common robustness and weakness points of both the DNNs and the attacks. Furthermore, realistic looking stickers applied by normal people as acts of vandalism are not studied to discover their potential risks as well the risk of optimizing the location of such realistic stickers to achieve the maximum performance drop. In this paper, (a) we study the case of realistic looking sticker application effects on traffic sign detectors performance; (b) we use traffic sign image classification as our use case and train and attack 11 of the modern architectures for our analysis; (c) by considering different factors like brightness, blurriness and contrast of the train images in our sticker application procedure, we show that simple image processing techniques can help realistic looking stickers fit into their background to mimic real world tests; (d) by performing structured synthetic and real-world evaluations, we study the difference of various traffic sign classes in terms of their crucial distinctive features among the tested DNNs.

Deep Neural Networks (DNNs) for perception in automated driving have been extensively studied, while achieving strong results in detection performance on pre-annotated test sets. However, there has been a gap in the literature on a systematic analysis of DNNs behavior to investigate the factors contributing to their misbehavior. As part of DNNs safety, we propose to both analyze DNNs behavior in challenging scenarios as well as the respective factors that actually contribute to their misbehavior. Although some of such factors have been studied individually, there is not a thorough study to compare all together in a systematic manner to unveil the impact of each factor leading to DNNs failures. In this paper, we propose an approach to evaluate the DNNs performance limiting factors (PLF), and their contribution to the DNNs misbehavior. Accordingly, we analyze seventeen factors from the literature, introduce four novel factors and conduct an assessment on all of them to assess their potential as a PLF. Furthermore, we evaluate our results based on six state-of-the-art pedestrian detection DNNs including three detection tasks. For our experiments, we study a synthetic as well as a real-world dataset for pedestrian detection. We show that there exist various similarities and dissimilarities when comparing the PLF from a synthetic dataset to a real one, and discuss the causes and effects of such relations. Furthermore, we provide an approach to analyze the common factors from both real-world as well as synthetic datasets which might have similar effects on various DNNs performance.

Not only correct scene understanding, but also ability to understand the decision making process of neural networks is essential for safe autonomous driving. Current work mainly focuses on uncertainty measures, often based on Monte Carlo dropout, to gain at least some insight into a models confidence. We investigate a mixture of experts architecture to achieve additional interpretability while retaining comparable result quality.By being able to use both the overall model output as well as retaining the possibility to take into account individual expert outputs, the agreement or disagreement between those individual outputs can be used to gain insights into the decision process. Expert networks are trained by splitting the input data into semantic subsets, e.g. corresponding to different driving scenarios, to become experts in those domains. An additional gating network that is also trained on the same input data is consequently used to weight the output of individual experts. We evaluate this mixture of expert setup on the A2D2 dataset and achieve similar results to a baseline FRRN network trained on all available data, while getting additional information.